Minimax Estimation of the Mean Matrix of the Matrix Variate Normal Distribution under the Divergence Loss Function

IF 1.6 Q1 STATISTICS & PROBABILITY Statistica Pub Date : 2018-03-29 DOI:10.6092/ISSN.1973-2201/6956
S. Zinodiny, Sadegh Rezaei, S. Nadarajah
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引用次数: 2

Abstract

The problem of estimating the mean matrix of a matrix-variate normal distribution with a covariance matrix is considered under two loss functions. We construct a class of empirical Bayes estimators which are better than the maximum likelihood estimator under the first loss function and hence show that the maximum likelihood estimator is inadmissible. We find a general class of minimax estimators. Also we give a class of estimators that improve on the maximum likelihood estimator under the second loss function and hence show that the maximum likelihood estimator is inadmissible.
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散度损失函数下矩阵变量正态分布均值矩阵的极大极小估计
研究了在两种损失函数下用协方差矩阵估计矩阵变量正态分布的平均矩阵的问题。构造了一类在第一损失函数下优于极大似然估计的经验贝叶斯估计,从而证明了极大似然估计是不可容许的。我们找到了一类一般的极大极小估计。在二阶损失函数下,我们给出了一类改进极大似然估计的估计,从而证明了极大似然估计是不可容许的。
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来源期刊
Statistica
Statistica STATISTICS & PROBABILITY-
CiteScore
1.70
自引率
0.00%
发文量
0
审稿时长
10 weeks
期刊最新文献
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